NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach
Objective: Neonatal intensive care unit (NICU) admission among term neonates is associated with significant morbidity and mortality, as well as high healthcare costs. A comprehensive NICU admission risk assessment using an integrated statistical approach for this rare admission event may be used to...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Journal of Clinical Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0383/11/15/4258 |
| _version_ | 1797413495268966400 |
|---|---|
| author | Shahar Talisman Joshua Guedalia Rivka Farkash Tehila Avitan Naama Srebnik Yair Kasirer Michael S. Schimmel Dunia Ghanem Ron Unger Sorina Grisaru Granovsky |
| author_facet | Shahar Talisman Joshua Guedalia Rivka Farkash Tehila Avitan Naama Srebnik Yair Kasirer Michael S. Schimmel Dunia Ghanem Ron Unger Sorina Grisaru Granovsky |
| author_sort | Shahar Talisman |
| collection | DOAJ |
| description | Objective: Neonatal intensive care unit (NICU) admission among term neonates is associated with significant morbidity and mortality, as well as high healthcare costs. A comprehensive NICU admission risk assessment using an integrated statistical approach for this rare admission event may be used to build a risk calculation algorithm for this group of neonates prior to delivery. Methods: A single-center case–control retrospective study was conducted between August 2005 and December 2019, including in-hospital singleton live born neonates, born at ≥37 weeks’ gestation. Analyses included univariate and multivariable models combined with the machine learning gradient-boosting model (GBM). The primary aim of the study was to identify and quantify risk factors and causes of NICU admission of term neonates. Results: During the study period, 206,509 births were registered at the Shaare Zedek Medical Center. After applying the study exclusion criteria, 192,527 term neonates were included in the study; 5292 (2.75%) were admitted to the NICU. The NICU admission risk was significantly higher (ORs [95%CIs]) for offspring of nulliparous women (1.19 [1.07, 1.33]), those with diabetes mellitus or hypertensive complications of pregnancy (2.52 [2.09, 3.03] and 1.28 [1.02, 1.60] respectively), and for those born during the 37th week of gestation (2.99 [2.63, 3.41]; <i>p</i> < 0.001 for all), adjusted for congenital malformations and genetic syndromes. A GBM to predict NICU admission applied to data prior to delivery showed an area under the receiver operating characteristic curve of 0.750 (95%CI 0.743–0.757) and classified 27% as high risk and 73% as low risk. This risk stratification was significantly associated with adverse maternal and neonatal outcomes. Conclusion: The present study identified NICU admission risk factors for term neonates; along with the machine learning ranking of the risk factors, the highly predictive model may serve as a basis for individual risk calculation algorithm prior to delivery. We suggest that in the future, this type of planning of the delivery will serve different health systems, in both high- and low-resource environments, along with the NICU admission or transfer policy. |
| first_indexed | 2024-03-09T05:18:47Z |
| format | Article |
| id | doaj.art-06ac23ca81f348f980d5ce3e4ceb9f31 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T05:18:47Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-06ac23ca81f348f980d5ce3e4ceb9f312023-12-03T12:42:18ZengMDPI AGJournal of Clinical Medicine2077-03832022-07-011115425810.3390/jcm11154258NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis ApproachShahar Talisman0Joshua Guedalia1Rivka Farkash2Tehila Avitan3Naama Srebnik4Yair Kasirer5Michael S. Schimmel6Dunia Ghanem7Ron Unger8Sorina Grisaru Granovsky9Shaare Zedek Medical Center, Department of Obstetrics & Gynecology, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelThe Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat-Gan 5290002, IsraelShaare Zedek Medical Center, Department of Obstetrics & Gynecology, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelShaare Zedek Medical Center, Department of Obstetrics & Gynecology, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelShaare Zedek Medical Center, Department of Obstetrics & Gynecology, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelShaare Zedek Medical Center, Department of Pediatrics, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelShaare Zedek Medical Center, Department of Pediatrics, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelShaare Zedek Medical Center, Department of Obstetrics & Gynecology, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelThe Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat-Gan 5290002, IsraelShaare Zedek Medical Center, Department of Obstetrics & Gynecology, School of Medicine, Hebrew University, Jerusalem 9103102, IsraelObjective: Neonatal intensive care unit (NICU) admission among term neonates is associated with significant morbidity and mortality, as well as high healthcare costs. A comprehensive NICU admission risk assessment using an integrated statistical approach for this rare admission event may be used to build a risk calculation algorithm for this group of neonates prior to delivery. Methods: A single-center case–control retrospective study was conducted between August 2005 and December 2019, including in-hospital singleton live born neonates, born at ≥37 weeks’ gestation. Analyses included univariate and multivariable models combined with the machine learning gradient-boosting model (GBM). The primary aim of the study was to identify and quantify risk factors and causes of NICU admission of term neonates. Results: During the study period, 206,509 births were registered at the Shaare Zedek Medical Center. After applying the study exclusion criteria, 192,527 term neonates were included in the study; 5292 (2.75%) were admitted to the NICU. The NICU admission risk was significantly higher (ORs [95%CIs]) for offspring of nulliparous women (1.19 [1.07, 1.33]), those with diabetes mellitus or hypertensive complications of pregnancy (2.52 [2.09, 3.03] and 1.28 [1.02, 1.60] respectively), and for those born during the 37th week of gestation (2.99 [2.63, 3.41]; <i>p</i> < 0.001 for all), adjusted for congenital malformations and genetic syndromes. A GBM to predict NICU admission applied to data prior to delivery showed an area under the receiver operating characteristic curve of 0.750 (95%CI 0.743–0.757) and classified 27% as high risk and 73% as low risk. This risk stratification was significantly associated with adverse maternal and neonatal outcomes. Conclusion: The present study identified NICU admission risk factors for term neonates; along with the machine learning ranking of the risk factors, the highly predictive model may serve as a basis for individual risk calculation algorithm prior to delivery. We suggest that in the future, this type of planning of the delivery will serve different health systems, in both high- and low-resource environments, along with the NICU admission or transfer policy.https://www.mdpi.com/2077-0383/11/15/4258neonatal intensive careterm NICUmachine learning |
| spellingShingle | Shahar Talisman Joshua Guedalia Rivka Farkash Tehila Avitan Naama Srebnik Yair Kasirer Michael S. Schimmel Dunia Ghanem Ron Unger Sorina Grisaru Granovsky NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach Journal of Clinical Medicine neonatal intensive care term NICU machine learning |
| title | NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach |
| title_full | NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach |
| title_fullStr | NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach |
| title_full_unstemmed | NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach |
| title_short | NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach |
| title_sort | nicu admission for term neonates in a large single center population a comprehensive assessment of risk factors using a tandem analysis approach |
| topic | neonatal intensive care term NICU machine learning |
| url | https://www.mdpi.com/2077-0383/11/15/4258 |
| work_keys_str_mv | AT shahartalisman nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT joshuaguedalia nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT rivkafarkash nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT tehilaavitan nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT naamasrebnik nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT yairkasirer nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT michaelsschimmel nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT duniaghanem nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT ronunger nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach AT sorinagrisarugranovsky nicuadmissionfortermneonatesinalargesinglecenterpopulationacomprehensiveassessmentofriskfactorsusingatandemanalysisapproach |